Automatic backlight compensation and exposure control

ABSTRACT

In some embodiments, images for the video conference may be white balanced and/or exposure controlled. White balancing may include dividing an image&#39;s pixels into multiple ranges according to their luminance and chrominance values. In some embodiments, blue and red analog gains may be modified to bring red and blue accumulations closer to green accumulations. In some embodiments, luminance values for an image may be adjusted by increasing or decreasing exposure. The decision whether to increase or decrease the exposure may be made by looking at statistics for luminance values of skin colored regions if there are sufficient skin colored regions on the image. Otherwise, luminance values for the entire image may be analyzed to determine whether to increase or decrease exposure.

PRIORITY

This application claims priority to U.S. Provisional Patent ApplicationSer. No. 60/619,210 titled “Video Conference Call System”, which wasfiled Oct. 15, 2004, whose inventors are Michael J. Burkett, AshishGoyal, Michael V. Jenkins, Michael L. Kenoyer, Craig B. Malloy, andJonathan W. Tracey.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates generally to video and, more specifically,to image enhancement.

2. Description of the Related Art

Images in video conference calls may suffer from exposure and/or whitebalance problems. Exposure problems may be caused by too much or toolittle light being received through the camera. White balance problemsmay be caused by incorrect ratios of colors in the image. Correctexposure may be more difficult to achieve than white balance, becausewhite balance may be easier to fix than exposure after the image isacquired.

Gray world algorithms and specular reflection algorithms are twocategories of white balance algorithms. Gray world algorithms may lookfor approximately the same amount of color in Red Green Blue (RGB) space(or equal Cb and Cr in YCC space) and adjust for any offsets. In YCCspace, Y is a measure of luminance (intensity of light per unit area),Cb is a measure of blue chrominance (luminance minus blue), and Cr is ameasure of red chrominance (luminance minus red). Specular reflectionmodels may assume that the brightest spots in an image reflect the lightsource neutrally and may adjust the spectrum of the image accordingly.Both gray world algorithms and specular reflection models havedisadvantages that may result in poor image quality.

SUMMARY OF THE INVENTION

In some embodiments, images for the video conference may be whitebalanced and/or exposure controlled. In some embodiments, pixels ofinterest may be used for determining a proper exposure correction and/orwhite balancing an image. Pixels of interest may include pixelsidentified with movement in the image, identified with skin portions inthe image, identified with portions of the image closest to the camera,identified with pixels corresponding to a speaking participant, and/oridentified with warm portions of the image. In some embodiments, thepixels for the image may be adjusted based on the exposure correctiondetermined for the pixels of interest.

In some embodiments, the exposure correction may be determined bycalculating a skew and a predicted peak, and then modifying the exposurebased on the calculated predicted peak. In some embodiments, a skew ofthe luminance values of at least a portion of the image may becalculated, and if the skew is above a predetermined value, a predictedpeak value may be calculated according to a first formula. If the skewis not above a predetermined value, the predicted peak value may becalculated according to a second formula. In some embodiments, if thepredicted peak value is less than a predetermined low predicted peakvalue, an exposure of the image may be increased, and if the predictedpeak value is more than a predetermined high predicted peak value, theexposure of the image may be decreased.

White balancing may include dividing an image's pixels into multipleranges according to their luminance and chrominance values. In someembodiments, a video image may be enhanced by modifying blue and redanalog gains to bring red and blue accumulations closer to greenaccumulations. In some embodiments, luminance values for an image may beadjusted by increasing or decreasing exposure. The decision whether toincrease or decrease the exposure may be made by looking at statisticsfor luminance values of skin colored regions if there are sufficientskin colored regions on the image. Otherwise, luminance values for theentire image may be analyzed to determine whether to increase ordecrease exposure.

BRIEF DESCRIPTION OF THE DRAWINGS

A better understanding of the present invention may be obtained when thefollowing detailed description is considered in conjunction with thefollowing drawings, in which:

FIG. 1 illustrates a video conference system, according to anembodiment;

FIG. 2 illustrates a method for exposure control for an image in avideoconference, according to an embodiment;

FIG. 3 illustrates a method for exposure control for an image, accordingto an embodiment;

FIG. 4 illustrates a method for evaluating the exposure of an imageusing statistics of the Y values for the image, according to anembodiment;

FIG. 5 illustrates a method for increasing or decreasing exposure,according to an embodiment; and

FIG. 6 illustrates a method for accumulating pixels in multipleaccumulators, according to an embodiment.

While the invention is susceptible to various modifications andalternative forms, specific embodiments thereof are shown by way ofexample in the drawings and will herein be described in detail. Itshould be understood, however, that the drawings and detaileddescription thereto are not intended to limit the invention to theparticular form disclosed, but on the contrary, the intention is tocover all modifications, equivalents, and alternatives falling withinthe spirit and scope of the present invention as defined by the appendedclaims. Note, the headings are for organizational purposes only and arenot meant to be used to limit or interpret the description or claims.Furthermore, note that the word “may” is used throughout thisapplication in a permissive sense (i.e., having the potential to, beingable to), not a mandatory sense (i.e., must). The term “include”, andderivations thereof, mean “including, but not limited to”. The term“coupled” means “directly or indirectly connected”.

DETAILED DESCRIPTION OF THE INVENTION

Incorporation by Reference U.S. Provisional Patent Application Ser. No.60/619,303, titled “Speakerphone”, which was filed Oct. 15, 2004, whoseinventors are Michael L. Kenoyer, William V. Oxford, and Simon Dudley ishereby incorporated by reference in its entirety as though fully andcompletely set forth herein.

U.S. Provisional Patent Application Ser. No. 60/619,212, titled “VideoConferencing Speakerphone”, which was filed Oct. 15, 2004, whoseinventors are Michael L. Kenoyer, Craig B. Malloy, and Wayne E. Mock ishereby incorporated by reference in its entirety as though fully andcompletely set forth herein.

U.S. Provisional Patent Application Ser. No. 60/619,227, titled “HighDefinition Camera and Mount”, which was filed Oct. 15, 2004, whoseinventors are Michael L. Kenoyer, Patrick D. Vanderwilt, Paul D. Frey,Paul Leslie Howard, Jonathan I. Kaplan, and Branko Lukic, is herebyincorporated by reference in its entirety as though fully and completelyset forth herein.

U.S. Provisional Patent Application Ser. No. 60/619,210, titled “VideoConference Call System”, which was filed Oct. 15, 2004, whose inventorsare Michael J. Burkett, Ashish Goyal, Michael V. Jenkins, Michael L.Kenoyer, Craig B. Malloy, and Jonathan W. Tracey is hereby incorporatedby reference in its entirety as though fully and completely set forthherein.

FIG. 1 illustrates an embodiment of a video conference system. In someembodiments, a camera 103 may capture an image of conferenceparticipants (e.g., local participant 107). Images of other conferenceparticipants (e.g., from remote conference sites) may be displayed onthe display 101. In some embodiments, the local image of the conferenceparticipant 107 may need to be white balanced and/or exposurecontrolled. In some embodiments, images from remote sites may also beenhanced. In some embodiments, image enhancement may be performed byelectronics in the camera 103 and/or codec 105. The electronics mayinclude circuitry, processors, memory, etc. for performing imageenhancement.

FIG. 2 illustrates a method for exposure control for an image in avideoconference, according to an embodiment. It is noted that in variousembodiments one or more of the method elements may be performedconcurrently, in a different order, or be omitted. Additional elementsmay be performed as desired.

At 201, video data may be received for display on a display device in avideoconference. In some embodiments, the video data may include aplurality of pixels corresponding to an image. The image may be aportion of an image in a video stream. In some embodiments, the imagemay include multiple images and/or multiple portions of an image (e.g.,multiple images in a chronological sequence may be considered together).The image may include at least one human participant image.

At 203, a first plurality of pixels corresponding to a first portion ofthe image may be determined. In some embodiments, the first plurality ofpixels may be pixels of interest in the image. In some embodiments, thefirst plurality of pixels may be determined by determining which pixelsof the image to exclude from consideration (e.g., pixels that are not ofinterest).

In some embodiments, the plurality of pixels may correspond to pixels ofa moving object in the image. In some embodiments, algorithms (e.g.,implemented in hardware and/or software) may be implemented to determinewhich pixels are of interest (e.g., correspond to a moving object). Forexample, in some embodiments, the algorithm may detect pixelscorresponding to a moving object by looking for pixels changingluminance values more dramatically and/or more quickly than other pixelsof the image.

In some embodiments, the first plurality of pixels may be pixels thatcorrespond to skin portions of the human participant. For example,ranges of pixel values (depending on the color space used) may correlatemore closely to human skin than other values. While dark skin and lightskin may have different luminance values, in YCC space, they may havesimilar ranges of blue and red chrominance values.

In some embodiments, the first plurality of pixels may correspond toobjects that are closest to the camera. For example, the pixels of anobject closest to a camera may be determined using two or more camerasand the parallax effect. In some embodiments, measurement equipment maybe used to detect the distance of objects from the camera (e.g., a laserand sensor). These distance measurements may be correlated with thepixels in the image and the first plurality of pixels may be designatedas the pixels that correlate to the closest objects.

In some embodiments, a camera position relative to an array ofmicrophones coupled to the videoconference system may be known, and thefirst plurality of pixels may be determined based on identified portionscorresponding to one or more participants in the image who are speaking.The algorithm may correlate the positions of the participants who arespeaking (e.g., determined through beamforming) relative to the camerato determine the first plurality of pixels.

In some embodiments, a temperature sensor (e.g., an infrared detector)may be coupled to the camera. The first plurality of pixels may bedetermined based on which pixels in the image correspond to warmportions of the image (i.e., which may correlate to human bodies thatare usually warmer than their surroundings).

In some embodiments, the pixels in a certain portion of an image may beused for the first plurality of pixels. For example, the pixels in thecenter of the image may be used as the first plurality of pixels.

In some embodiments, other methods for finding pixels of interest may beused. In some embodiments, two or more methods may be used inconjunction. In some embodiments, a method for determining the pixels ofinterest may be stored and used to determine whether to use the samemethod on the next frame or whether to switch methods (e.g., the methodmay be switched if a sufficient number of pixels of interest is notfound).

At 205, the first plurality of pixels may be examined to determine anexposure correction for the first plurality of pixels. For example, theluminance values of the pixels of interest may be analyzed to determineif there are luminance values above a predetermined upper limit. If not,the exposure correction may indicate that the exposure of the imageneeds to be increased. In some embodiments, if there are luminancevalues above the predetermined upper limit, then the pixels may beanalyzed to determine if there are pixels below a predetermined lowerlimit. If not, the exposure correction may indicate that the exposurefor the image needs to be decreased. In some embodiments, an average ofthe luminance values of the first plurality of pixels may be compared toa threshold to determine an exposure correction. In some embodiments, ahistogram of the luminance values of the first plurality of pixels maybe used to determine an exposure correction (e.g., the histogram may becompared to a predetermined histogram). The exposure correction may bebased on a correction needed to make the current histogram match thepredetermined histogram.

At 207, the plurality of pixels may be adjusted corresponding to theimage based on the exposure correction. In some embodiments, the entireimage may be adjusted based on the determined exposure correction.

FIG. 3 illustrates a method for exposure control for an image, accordingto an embodiment. It is noted that in various embodiments one or more ofthe method elements may be performed concurrently, in a different order,or be omitted. Additional elements may be performed as desired.

At 301, video data may be received for display on a display device in avideoconference. In some embodiments, the video data may include aplurality of pixels corresponding to at least one image. In someembodiments, the image may include at least one human participant image.

At 303, a first plurality of pixels corresponding to a first portion ofthe image may be determined. In some embodiments, the first plurality ofpixels may include pixels of interest. In some embodiments, the firstplurality of pixels may be based on identified skin portions of at leastone human participant image. In some embodiments, each of the pluralityof pixels may include a luminance value.

At 305, a skew of the luminance values of at least a portion of theimage may be calculated. It is to be understood that the term “skew” asused herein refers to a measurement relative to the third standardizedmoment of the distribution of the pixel values. For example, the skew ofthe Y values may be calculated according to:skew=(average−median)/standard deviationWhere standard deviation is calculated as:${{standard}\quad{deviation}} = \sqrt{\sum\limits_{N}^{i}\quad\frac{\left( {x_{i} - {average}} \right)^{2}}{N - 1}}$where N may be the total number of samples. Other calculations forstandard deviation and skew may also be used.

At 307 a determination may be made whether the skew is above apredetermined value.

At 309, if the skew is above a predetermined value, a predicted peakvalue may be calculated according to a first formula. In someembodiments, according to the first formula, the predicted peak valuemay be approximately an average of the luminance values minus a standarddeviation of the luminance values times a skew of the luminance values(predicted peak value=average−(standard deviation*skew)). In someembodiments, the predicted peak value may not be a realistic luminancevalue.

At 311, if the skew is not above a predetermined value, the predictedpeak value may be calculated according to a second formula. In someembodiments, according to the second formula, the predicted peak valuemay be approximately a total of the luminance values divided by thenumber of luminance values.

At 313, a determination may be made whether the predicted peak value isless than a predetermined low predicted peak value.

At 315, if the predicted peak value is less than a predetermined lowpredicted peak value, an exposure of the image may be increased.

At 317, if the predicted peak value is more than a predetermined highpredicted peak value, the exposure of the image may be decreased.

FIG. 4 illustrates a method for evaluating the exposure of an imageusing statistics of the Y values for the image, according to anembodiment. In various embodiments, the Y values for the entire image orfor only pixels of interest (e.g., skin colored pixels) may beevaluated. It is noted that in various embodiments one or more of themethod elements may be performed concurrently, in a different order, orbe omitted. Additional elements may be performed as desired.

At 401, a percentage of image pixels with red chrominance (Cr) and bluechrominance (Cb) in a predetermined skin range may be determined. Insome embodiments, regions of the image with Cb between 75 and 128 and Crbetween 130 and 170 may be considered skin colored.

At 403, if the percentage of image pixels Cr and Cb in the predeterminedrange is above a minimum skin percentage, at 405, a skew of the Y valuesfor the pixels in the predetermined skin range may be calculated. Insome embodiments, a minimum percentage of 5 percent may be used. Otherpercentages may also be used.

At 407, a determination may be made whether the skew is above apredetermined skew minimum. For example, a minimum skew of 0.5 may beused. Other skew minimums may also be used.

At 409, if the skew is above 0.5, the predicted peak value may becalculated according to a first formula (predicted peakvalue=average−(standard deviation* skew)). In some embodiments, otherformulas may be used to calculate the predicted peak value.

At 411, if the skew is not above the skew minimum, an average Y (i.e., asecond formula→predicted peak value=total of all Y values/number of Yvalues) for the pixels in the predetermined range may be used as thepredicted peak value.

At 455, if the percentage of image pixels Cr and Cb in the predeterminedrange is not above a minimum percentage, the skew for the entire imagemay be calculated.

At 457, a determination may be made whether the skew calculated at 455is above a predetermined skew minimum. For example, a skew minimum of0.5 may be used.

At 459, if the calculated skew is above the predetermined skew minimum,the predicted peak value may be calculated according to the firstformula:predicted peak value=average−(standard deviation*skew)

At 461, if the calculated skew is not above the predetermined skewminimum, the average Y value (i.e., the second formula→predicted peakvalue=total of all Y values/number of Y values) for the entire image maybe used as the predicted peak value.

At 413, if the predicted peak value is less than a predetermined lowpredicted peak value (e.g., 60), at 415, the exposure may be increased.In some embodiments, the image may be adjusted such that the Y values ofthe skin are spread over a reasonable range.

At 417, if the predicted peak value is greater than a predetermined highpredicted peak value (e.g., 200), at 421 the exposure may be decreased.

At 423, the system may wait for the next frame.

In various embodiments, an auto exposure control may be used to adjustthe Y values. In some embodiments, the auto exposure control may havetwo components to evaluate exposure and to modify exposure. Autoexposure control may manipulate several mechanisms to achieve properexposure. In some embodiments, an aperture stop that controls lightpassing through the lens may be manipulated by the auto exposurecontrol. For example, the aperture stop may be approximately in therange of f3.5 to f16. The wider the aperture stop is open, the more thedepth of field is reduced. Reducing the aperture may increase the depthof field but may also let less light through. In some embodiments,integration time (i.e., how long light is allowed to accumulate on eachpixel) may also be manipulated by the auto exposure control. Theintegration time may be extended as long as possible while achieving therequired frame rate. Longer integration times may produce lower noise inthe image, but may reduce frame rate. In some embodiments, to achieve 30frames per second, a maximum desired integration time may be less than33 milliseconds. In some embodiments, the auto exposure control may alsomanipulate gain in the amplifiers converting analog voltages intodiscrete binary values. In some embodiments, the analog gain may beapproximately in the range of 1 to 15. However, in some embodiments,increasing the analog gain may amplify noise. The auto exposure controlmay also manipulate digital gain. Digital gain may also amplify noise.

FIG. 5 illustrates a method for increasing or decreasing exposure,according to an embodiment. It is noted that in various embodiments oneor more of the method elements may be performed concurrently, in adifferent order, or be omitted. Additional elements may be performed asdesired.

At 501, the exposure may be evaluated.

At 503, if the exposure needs to be decreased, several possible methodsmay be selected. In some embodiments, if a method is selected, after themethod is applied, the flow may return to 501 where the exposure mayagain be evaluated. In some embodiments, one of the methods foradjusting exposure may be applied multiple times to the same image. Inaddition, multiple different methods to adjust exposure may be applied.

At 505, if the integration time is greater than a predetermined minimumintegration time (e.g., 33 ms), the integration time may be reduced at507.

At 511, if digital gain is available, the digital gain may be reduced at513. The exposure correction may thus be accomplished through pixelgain.

At 517, if analog gain is available, at 519, the analog gain may bereduced.

At 525, if the iris is open, at 527, the aperture of the iris may bereduced.

If none of the above methods are available, or if the exposure stillneeds to be decreased after one or more of the above methods is applied,the integration time may be further reduced.

At 509, if the exposure needs to be increased, several possible methodsmay be selected.

At 511, if the integration time is below a predetermined minimumintegration time (e.g., 33 ms), the integration time may be increased at521.

At 523, if the iris is closed, at 529, the iris may be opened.

At 531, if there is analog gain left, at 533, the analog gain may beincreased.

At 537, if there is digital gain left, at 539, the digital gain may beincreased.

If none of the above methods are available, or if the exposure stillneeds to be increased after one or more of the above methods is applied,the integration time may be further increased if the integration time isless than a predetermined maximum integration time (e.g., 100 ms).

White Balance

In some embodiments, white balance control may include controlling arange of luminance values and a range of chrominance values for an imageto be displayed in a conference call. In some embodiments, a variant ofthe gray world algorithm and the specular reflection model may be used.This may include identifying a likely light source in an image andcorrecting the luminance and chrominance values in the image. Forexample, a light source may include direct sun, clear sky, cloudy sky,incandescent lamps, or other lamps. Other light sources are alsopossible.

To white balance the image, a correcting algorithm may adjust an imageby classifying the pixels of the image into different ranges accordingto characteristic values of the pixels. For example, if using a YCCcolor space, the pixels may be classified into different ranges ofluminance (Y), blue chrominance (Cb), and red chrominance (Cr). Othercolor spaces may also be used. Then, depending on the percentage ofpixels in each of the different ranges, the image may be correctedaccordingly.

FIG. 6 illustrates a method for accumulating pixels in multipleaccumulators, according to an embodiment. It is noted that in variousembodiments one or more of the method elements may be performedconcurrently, in a different order, or be omitted. Additional elementsmay be performed as desired.

In various embodiments, the pixels may be classified into a bright,brighter, and brightest range. In some embodiments, other numbers ofranges may be used (e.g., four or greater or two or less). In someembodiments, each range may have a Y lower bound (e.g., Y1lo, Y2lo, andY3lo) and a Y upper bound (e.g., Y1hi, Y2hi, and Y3hi). Each range mayalso have a Cb upper bound (Cb1hi, Cb2hi, and Cb3hi) and a Cb lowerbound (Cb1lo, Cb2lo, and Cb3lo) for Cb, and a Cr upper bound (Cr1hi,Cr2hi, and Cr3hi) and Cr lower bound (Cr1lo, Cr2lo, and Cr3lo) for Cr.The pixels may thus be separated into three ranges (one range with thebrightest pixels, then the next brightest pixels, and finally the leastbrightest pixels). Some pixels may not be in any of the three ranges. Insome embodiments, each range may be assigned three accumulators (i.e.,in the first range, one for luminance range Y1lo to Y1hi, one for Cbchrominance range Cb1lo to Cb1hi, and one for Cr chrominance range Cr1loto Cr1hi). There may be a total of nine accumulators (3 more for thesecond range and three more for the third range). In some embodiments,other numbers of accumulators may be used.

In some embodiments, nine accumulators and three counters may be used tocategorize the pixels of an image according to brightness andchrominance. Other numbers of accumulators and counters may also beused. In some embodiments, four register sets may be programmed bysoftware to sort the pixels into the three acceptance ranges (e.g., (Y1,Cb1, Cr2), (Y2, Cb2, Cr2), and (Y3, Cb3, Cr3)). Other numbers ofacceptance ranges and register sets may also be used.

In some embodiments, pixels may be converted from one color space toanother (e.g., from RGB space to YCC space). At 601, the Y, Cb, and Crmay be compared to the three value ranges. In some embodiments, Y, Cb,and Cr may be provided as arbitrary numbers (e.g., relative to anexposure level of the image). At 603, the Y of the pixel may be comparedto the Y3lo to Y3hi range (e.g., 210 to 230). Again, it is to be notedthat the numbers 210 and 230 may be arbitrary and may be dependent onthe current exposure of the image and the units used for Y, Cb, and Cr(or other color spaces if used). If it has a Y in that range, at 605,the Cb of the pixel may be compared to the Cb3lo to Cb3hi range (e.g.,108 to 148). If the Cb of the pixel is in that range, at 607, the Cr ofthe pixel may be compared to the Cr3lo to Cr3hi range (e.g., 108 to148). If the Cr of the pixel is in that range, each of the threeaccumulators (one for Y3, one for Cb3, and one for Cr3) may store theirrespective RGB value for the pixel. In addition, at 609, the counter forthis set of ranges may be incremented by one.

If the Y of the pixel is not in the Y3lo to Y3hi range, at 611, it iscompared to the Y2lo to Y2hi range (e.g., 170 to 210). If it has a Y inthat range, at 613, it may be compared to the Cb2lo to Cb2hi range(e.g., 98 to 158). If the Cb of the pixel is in that range, at 615, itmay be compared to the Cr2lo to Cr2hi range (e.g., 98 to 158). If the Crof the pixel is in that range, each of the three accumulators (one forY3, one for Cb3, and one for Cr3) may store their respective RGB valuefor the pixel. In addition, at 617, the counter for this set of rangesmay be incremented by one.

If the Y of the pixel is not in the Y3lo to Y3hi range, at 621, it iscompared to the Y1lo to Y1hi range (e.g., 130 to 170). If it has a Y inthat range, at 623, the Cb for the pixel may be compared to the Cb1lo toCb1hi range (e.g., 88 to 168). If the Cb of the pixel is in that range,at 625, the Cr of the pixel may be compared to the Cr1lo to Cr1hi range(e.g., 88 to 168). If the Cr of the pixel is in that range, each of thethree accumulators (one for Y3, one for Cb3, and one for Cr3) may storetheir respective value for the pixel (which may be an RGB value). Inaddition, at 627, the counter for this set of ranges may be incrementedby one. In some embodiments, pixels with Y below Y1lo or above the Y3himay be ignored.

In some embodiments, if a sufficient number of pixels (e.g.,approximately 0.5%) are in the brightest Y range (e.g., approximately210 to 230, chrominance between 108 and 148) those accumulations may beused to determine how much to adjust the pixels of the image in order tocorrect the white balance for the image. In some embodiments, thepercentage may be determined using the counter for the range as comparedto a total number of pixels for the image. A baseline percentage, e.g.,0.5%, may be predetermined for comparison to the percentage of pixelsfound in the brightest range. Other percentages may also be used.Because pixels in the brightest range should be the closest to white,the chrominance ranges for Cr and Cb may be relatively small with 128(neutral) as the center. If there are not at least 0.5% of the pixels inthis range, the next range (e.g., Y between 170 and 210, chrominance(for Cr and Cb) between 98 and 158) may be examined. Because this rangeis not as bright as the first range, a larger range (with 128 in thecenter) may be used for the chrominance because values in this range maybe further from neutral. In some embodiments, more pixels may need to bepresent in this range (e.g., a baseline percentage of approximately 1%)for these pixels to be used for white balance. Other percentages mayalso be used for the comparison. In some embodiments, the last range mayneed approximately 2% (Y between 130 and 170, chrominance between 88 and168). While examples of percentages have been provided, it is to beunderstood that the percentages may also be variable (i.e., thepercentages may be adjusted based on factors such as previous imageexposure).

If the percentages for these ranges are not exceeded, the software maydo nothing. In some embodiments, if a percentage for one of the rangesis exceeded, the blue and red gains (e.g., analog and/or digitalfilters) of sensors for the camera may be modified by a ratio accordingto a ratio needed to bring red and blue accumulation values closer togreen. For example, if sensor values for the green sum is 56711, the redsum is 64379, and the blue sum is 48925, the image may have too muchred, and not enough blue (i.e., white may have equal amounts of red,green, and blue). In some embodiments, the red gain may be adjusted by(56711/64379) or 0.8809, and the blue gain may be adjusted by(56711/48925) or 1.159. Other ratios and calculations may also be usedto adjust the gains.

In some embodiments, the sensors may be adjusted by other ratios. Forexample, the ratios may be dependent on which corresponding percentagewas exceeded. In some embodiments, the amount of adjustment may be basedon the values of Y and chrominance (stored in the accumulations) for thepixels in the accepted range. For example, in some embodiments, weightsmay be applied based on the percentage of pixels in a particular rangeand/or based on the actual values of Y and chrominance for pixels in aparticular range. Other criteria may also be used. In some embodiments,this characterization of the image may be done when software sets acontrol bit. In some embodiments, one frame of data may be analyzed at atime. The counters and accumulators may be cleared at the start of eachframe when the control is set.

In various embodiments, the distribution of Y values for an image may beevaluated to determine correct exposure. In some embodiments, ahistogram of values for the image may be used to determine whether theimage has proper exposure. For example, it may be desirable to have animage with Y values above 230 and/or values of below 40.

In various embodiments, the image exposure may need correction if Yvalues are not in certain ranges. For example, some video standards(e.g. BT656) may only allow certain ranges of Y values (e.g., between 16(black) and 240 (white)). Other ranges may also be used. In someembodiments, the broader the range of Y values, the better the image.For example, an image with only values between 16 and 40 may be verydark, while an image with values between 200 and 240 may be very white.

In some embodiments, even when the image has Y values in a broad rangeof histograph bins (e.g., from 16 to 240), it may still need exposuremodification. For example, backlighting may require modifying exposureeven when the bins have reasonable values. Windows behind people in aconference room may provide more light than the lights in the room. Thismay cause a highly non-uniform distribution of Y values.

Further modifications and alternative embodiments of various aspects ofthe invention may be apparent to those skilled in the art in view ofthis description. Accordingly, this description is to be construed asillustrative only and is for the purpose of teaching those skilled inthe art the general manner of carrying out the invention. It is to beunderstood that the forms of the invention shown and described hereinare to be taken as embodiments. Elements and materials may besubstituted for those illustrated and described herein, parts andprocesses may be reversed, and certain features of the invention may beutilized independently, all as would be apparent to one skilled in theart after having the benefit of this description of the invention.Changes may be made in the elements described herein without departingfrom the spirit and scope of the invention as described in the followingclaims.

1. A method for exposure control for an image in a videoconference,comprising: receiving video data for display on a display device in avideoconference, wherein the video data comprises a plurality of pixelscorresponding to at least one image, wherein the at least one imagecomprises at least one human participant image; determining a firstplurality of pixels corresponding to a first portion of the image;examining the first plurality of pixels to determine an exposurecorrection for the first plurality of pixels; adjusting the plurality ofpixels corresponding to at least one image based on the exposurecorrection.
 2. The method of claim 1, wherein said determining the firstplurality of pixels is performed based on identified movement in theimage.
 3. The method of claim 1, wherein said determining the firstplurality of pixels is performed based on identified skin portions ofthe at least one human participant image.
 4. The method of claim 1,wherein said determining the first plurality of pixels is performedbased on identified portions of the image that are closest to thecamera.
 5. The method of claim 1, wherein said determining the firstplurality of pixels is performed based on identified portionscorresponding to one or more participants in the image who are speaking.6. The method of claim 1, wherein said determining the first pluralityof pixels is performed based on identified warm portions of the image.7. A method for exposure control for an image, comprising: calculating askew of the luminance values of at least a portion of the image; if theskew is above a predetermined value, calculating a predicted peak valueaccording to a first formula; if the skew is not above a predeterminedvalue, calculating the predicted peak value according to a secondformula; if the predicted peak value is less than a predetermined lowpredicted peak value, increasing an exposure of the image; and if thepredicted peak value is more than a predetermined high predicted peakvalue, decreasing the exposure of the image.
 8. The method of claim 7,wherein according to the first formula, the predicted peak value isapproximately an average of the luminance values minus a standarddeviation of the luminance values times a skew of the luminance values.9. The method of claim 7, wherein according to the second formula, thepredicted peak value is approximately a total of the luminance valuesdivided by the number of luminance values.
 10. The method of claim 7,wherein the skew is calculated for a portion of the image that comprisespixels of interest.
 11. The method of claim 10, wherein the pixels ofinterest are selected from a group consisting of identified movement inthe image, identified skin portions in the image, identified portions ofthe image closest to the camera, pixels corresponding to a speakingparticipant, and identified warm portions of the image.
 12. A method forexposure control for an image, comprising: receiving video data fordisplay on a display device in a videoconference, wherein the video datacomprises a plurality of pixels corresponding to at least one image,wherein the at least one image comprises at least one human participantimage; determining a first plurality of pixels corresponding to a firstportion of the image, wherein said determining the first plurality ofpixels is performed based on identified skin portions of the at leastone human participant image, wherein each of the plurality of pixelscomprises a luminance value; calculating a skew of the luminance valuesof the first plurality of pixels; if the skew is above a predeterminedvalue, calculating a predicted peak value of the luminance valuesaccording to a first formula; if the skew is not above a predeterminedvalue, calculating the predicted peak value of the luminance valuesaccording to a second formula; if the predicted peak value is less thana predetermined low predicted peak value, increasing an exposure of theimage; and if the predicted peak value is more than a predetermined highpredicted peak value, decreasing the exposure of the image.
 13. A methodfor exposure control for an image, comprising: receiving video data fordisplay on a display device in a videoconference, wherein the video datacomprises a plurality of pixels corresponding to at least one image,wherein the at least one image comprises at least one human participantimage; determining a first plurality of pixels corresponding to a firstportion of the image, wherein said determining the first plurality ofpixels is performed based on identified skin portions of the at leastone human participant image, wherein each of the plurality of pixelscomprises a luminance value; determining a percentage of the firstplurality of pixels versus the plurality of pixels comprising the image;if the percentage is above a minimum skin percentage, calculating a skewof the luminance values of the first plurality of pixels; if thepercentage is not above a minimum skin percentage, calculating a skew ofthe luminance values for the plurality of pixels; if the calculated skewis above a predetermined skew value, calculating a predicted peak valueaccording to a first formula using the luminance values used tocalculate the skew; if the skew is not above a predetermined skew value,calculating the predicted peak value according to a second formula usingthe luminance values used to calculate the skew; if the predicted peakvalue is less than a predetermined low predicted peak value, increasingan exposure of the image; and if the predicted peak value is more than apredetermined high predicted peak value, decreasing the exposure of theimage.
 14. The method of claim 13, wherein according to the firstformula, the predicted peak value is approximately an average of theluminance values minus a standard deviation of the luminance valuestimes a skew of the luminance values.
 15. The method of claim 13,wherein according to the second formula, the predicted peak value isapproximately a total of the luminance values divided by the number ofluminance values.
 16. A system, comprising: a camera; and electronicscoupled to the camera for performing a method comprising: receivingvideo data for display on a display device in a videoconference, whereinthe video data comprises a plurality of pixels corresponding to at leastone image, wherein the at least one image comprises at least one humanparticipant image; determining a first plurality of pixels correspondingto a first portion of the image, wherein said determining the firstplurality of pixels is performed based on identified skin portions ofthe at least one human participant image, wherein each of the pluralityof pixels comprises a luminance value; determining a percentage of thefirst plurality of pixels versus the plurality of pixels comprising theimage; if the percentage is above a minimum skin percentage, calculatinga skew of the luminance values of the first plurality of pixels; if thepercentage is not above a minimum skin percentage, calculating a skew ofthe luminance values for the plurality of pixels; if the calculated skewis above a predetermined skew value, calculating a predicted peak valueaccording to a first formula using the luminance values used tocalculate the skew; if the skew is not above a predetermined skew value,calculating the predicted peak value according to a second formula usingthe luminance values used to calculate the skew; if the predicted peakvalue is less than a predetermined low predicted peak value, increasingan exposure of the image; and if the predicted peak value is more than apredetermined high predicted peak value, decreasing the exposure of theimage.
 17. The system of claim 16, wherein according to the firstformula, the predicted peak value is approximately an average of theluminance values minus a standard deviation of the luminance valuestimes a skew of the luminance values.
 18. The system of claim 17,wherein according to the second formula, the predicted peak value isapproximately a total of the luminance values divided by the number ofluminance values.
 19. The system of claim 17, wherein the minimum skinpercentage varies based on one or more image characteristics.
 20. Thesystem of claim 19, wherein the image characteristic is previous imageexposure.